HDI vs. Expected Years of Education

Education is widely considered to have a strong contribution to the advancements of societies and the quality of life of the people who live in them. In the definition of the HDI measurement mentioned above, being knowledgeable is a contributing factor to a country’s HDI value. Curious to see how the length of time spent in school affects the UN’s measure of knowledge and a country’s HDI, we plotted countries’ HDI vs expected years of schooling to observe the trend between the two.

## `geom_smooth()` using formula = 'y ~ x'

Note: The points that belong to the NA continent group are countries that were unsuccessfully matched with a continent during the data wrangling process.

The linear regression of the plotted points has an \(R^2\) of 0.802, supporting the assumption of a strong correlation between the two variables. However, there are clearly some other factors that must influence the HDI as we can see that Europe has more countries plotted over the regression while Africa has more countries plotted below the regression. Although we know the main factors that are used to measure HDI, these results point towards regional variables influencing of HDI (could be issues accessing resources, emigration, etc.). In the future, we’d be curious to explore what the issues common issues among the countries below the regression and common accomplishments among countries above the regression.

Visualizations

Visualizations, particularly interactive ones, will be well-received. That said, do not overuse visualizations. You may be better off with one complicated but well-crafted visualization as opposed to many quick-and-dirty plots. Any plots should be well-thought-out, properly labeled, informative, and visually appealing.

If you want to include dynamic visualizations or tables, you should explore your options from packages that are built from htmlwidgets. These htmlwidgets-based packages offer ways to build lighterweight, dynamic visualizations or tables that don’t require an R server to run! A more complete list of packages is available on the linked website, but a short list includes:

• plotly: Interactive graphics with D3
• leaflet: Interactive maps with OpenStreetMap
• dygraphs: Interactive time series visualization
• visNetwork: Network graph visualization vis.js
• sparkline: Small inline charts
• threejs: Interactive 3D graphics

You may embed a published Shiny app in your blog if useful, but be aware that there is a limited window size for embedded objects, which tends to makes the user experience of the app worse relative to a dedicated Shiny app page. Additionally, Shiny apps will go idle after a few minutes and have to be reloaded by the user, which may also affect the user experience.

Any Shiny apps embedded in your blog should be accompanied by the link to the published Shiny app (I did this using a figure caption in the code chunk below, but you don’t have to incorporate the link in this way).

Tables

library(DT)
mtcars %>% 
  select(mpg, cyl, hp) %>% 
  datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
            filter = 'top',
            options = list(pageLength = 10, autoWidth = TRUE))

library(kableExtra)
summary(cars) %>%
  kbl(col.names = c("Speed", "Distance"),
      row.names = FALSE) %>%
  kable_styling(bootstrap_options = "striped",
                full_width = FALSE) %>%
  row_spec(0, bold = TRUE) %>%
  column_spec(1:2, width = "1.5in") 

Images

Images and gifs can be displayed using code chunks:

“Safe Space” by artist Kenesha Sneed

This is a figure caption

You may also use Markdown syntax for displaying images as shown below, but code chunks offer easier customization of the image size and alignment.

This is another figure caption

Either way, the file path can be a local path within your project directory or a URL for an image hosted online. This syntax works for PNG, PDF, JPG, and even GIF formats.

Videos

You can use code chunks or Markdown syntax include links to any valid YouTube or Vimeo URLs (see here for details) or point to a location within your project directory.

Code chunk:

Markdown syntax:

You may need to push your updates to GitHub to see if the videos work.

Equations

You might include equations if part of the purpose of your blog is to explain a statistical method. There are two ways to include equations:

• Inline: \(b \sim N(0, \sigma^2_b)\)
• Display-style (displayed on its own line): \[\frac{\sigma^2_b}{\sigma^2_b + \sigma^2_e}\]

For typesetting equations appropriately, check out the AMS-LaTeX quick reference or take a look at the Symbols in math mode section of this cheat sheet (or do some extra Googling—there are many resources).

Column formatting

Theme

You can use the rmdformats package to play around with some pre-built themes. There are, I’m sure, many many many more similar packages with built in themes, or you can look into how to include a CSS code chunk to customize aspects of a theme.

Using the rmdformats package, you can change the theme itself (Line 6):

• rmdformats::readthedown
• rmdformats::downcute
  • For downcute only, you can add a new indented line below Line 6 with the code downcute_theme: "chaos" for the downcute chaos theme
• rmdformats::robobook
• rmdformats::material

You can explore additional YAML options by looking at the rmdformats package page or running, for example, ?rmdformats::readthedown() to see the help documentation for a particular theme from the package.

Synax highlighting

You can also change the code chunk syntax highlighting option (Line 7, highlight):

• "default"
• "tango"
• "pygments"
• "kate"
• "monochrome"
• "espresso"
• "zenburn"
• "haddock"
• "textmate"
• NULL for no syntax highlighting (not recommended)

Font size, type, and other customization

Further customization requires adding a CSS style file or code chunk or incorporating other development options. Customization beyond the rmdformats package should be your lowest and final priority for the project. Ensure your content is fully prepared first.

Why wont HDI suffice?

Amartya Sen, a Nobel laureate and renowned economist, once quoted, “empowering women is the key to building the future we want.” This simple yet powerful statement highlights the significance of gender equality and its impact on human development. The notion of human development is rooted in the idea of expanding people’s choices, enabling them to fulfill their potential, and giving them the freedom to lead lives they value. However, the reality is that women’s choices and freedoms are not equally and they continue to be marginalized across the globe.

While countries with higher HDI ranks are generally associated with greater levels of freedom and empowerment, the reality is more complex. For instance, a country’s overall HDI score may mask significant disparities in gender inequality within its population. In many countries, women continue to face discrimination in areas such as education, employment, and political representation, despite their nation’s high HDI ranking. Moreover, the cultural and social norms prevalent in a country can significantly impact the empowerment of women, even in countries with high HDI scores. Therefore, while HDI rankings can provide a broad measure of a country’s level of human development, it is crucial to examine specific indicators that measure the empowerment of women to gain a more nuanced understanding of gender inequality across the globe.

To gain a better understanding of the complex issues that women face worldwide, we will analyze standardized indicators of women’s empowerment across countries based on their population and HDI rank. This analysis will reveal the factors that contribute to gender inequality and highlight areas for improvement to advance women’s empowerment and create a more equitable society.

Indicators of Women’s Empowerment

We will look into four key indicators of Women’s Empowement measure

1. Adolescent Birth Rate: This metric measures the number of births per 1,000 women between the ages of 15 and 19 in a given year. A high adolescent birth rate is often an indicator of poor sexual and reproductive health outcomes for young women, and can also be a barrier to educational and economic opportunities.

2. Political Participation: This metric measures the extent to which women are involved in political decision-making processes, including representation in elected offices, participation in political parties, and involvement in civil society organizations. Women’s political participation is important for ensuring that their voices and perspectives are heard in policy-making processes.

3. Labor Participation: This metric measures the percentage of women who are employed or seeking employment in the labor force. A low labor force participation rate can be an indicator of limited economic opportunities for women, which can in turn contribute to poverty and economic inequality.

4. Secondary Level Education: The women’s indicator of secondary level education is a metric that measures the percentage of women in a given population who have completed secondary education. This indicator is often used as a measure of women’s educational attainment and their access to educational opportunities.

Heatmap: HDI, Polulation, Key indicators of Women’s Empowerment

This heatmap represents data on different indicators of women’s empowerment across 30 most popullous countries, ranked according to their HDI (highest HDI rank in the top and lowest in the bottom). Each row and column of the heatmap represents a different country and a specific indicator, respectively. The colors in the heatmap represent different values of each indicator, with the lighter shades indicating lower values and the darker shades indicating higher values. The values are standardized to make the intepretations easier. For Example, darker colors indicate that a particular country is doing better on that indicator compared to countries with lighter colors. Additionally, dendrograms are included at the top and left sides of the heatmap, which show how countries and indicators are clustered together based on similarities in their values.

Key Interpretations

• The heatmap shows that there is generally a negative correlation between adolescent birthrate and HDI ranking, meaning that countries with higher development tend to have lower adolescent birthrates. However, the heatmap shows that it is not always the case. Some of the exceptions are Uganda and Nigeria, which have high adolescent birthrates despite their moderate development levels. Conversely, some countries with lower HDI rankings, such as India and Algeria, have lower adolescent birthrates

• The heatmap reveals that women’s political participation is not consistently correlated with a country’s HDI ranking. Contrary to the common belief that higher HDI ranking equates to greater political participation for women, the data shows that this is not always the case. For instance, countries like Mexico, with a lower HDI ranking, exhibit higher women’s political participation compared to Japan, which has a higher HDI ranking but a lower participation rate. This suggests that factors other than development, such as cultural and social norms, may play a role in determining women’s political participation. Therefore, a more nuanced and context-specific approach is necessary to understand the complex interplay between development and women’s political participation.

• The heatmap shows a strong positive correlation between women’s labor force participation and HDI ranking. Countries with higher HDI rankings tend to have higher labor force participation rates for women, while those with lower HDI rankings tend to have lower participation rates. However, it is worth noting that there are still significant disparities in women’s labor force participation rates within and across countries, even among those with high HDI rankings.

• The education level column of the heatmap, specifically reflecting the metric of women’s completion of secondary education, shows a positive correlation with HDI. In general, countries with higher HDI rankings tend to have higher rates of women completing secondary education, indicating greater access to educational opportunities and greater potential for personal and professional growth. However, there are exceptions to this trend. One such exception is Ethiopia, which ranks relatively high in the HDI spectrum but has very low rates of women completing secondary education. This indicates that while Ethiopia has made progress in areas such as healthcare and income, it may face challenges in ensuring equal access to education for women.